Precision Unveiled in Unborn: A Cutting-Edge Hybrid Machine Learning Approach for Fetal Health State Classification.

Abstract

Purpose: Understanding and categorizing fetal health is an influential field of research that profoundly impacts the well-being of both mother and child. The primary desire to precisely examine and cure fetal disorders during pregnancy to enhance fetal and maternal outcomes is the driving force behind the classification of fetal health. Fetal cardiac abnormalities (structural or functional) need immediate doctor attention, and their early identification and detection in all stages of pregnancy can help doctors with the timely treatment of the mother and the unborn child by enabling appropriate prenatal counseling and management. By knowing about fetal health and taking necessary precautions for fetal health, the rate of fetal mortality can be decreased. Advancements in machine learning (ML) algorithms have revolutionized the analysis of fetal electrocardiogram (ECG) signals. Machine Learning and Deep Learning algorithms automate the fetal monitoring process and decisions in emergencies, save time, and enable telemonitoring.

Methods: This paper introduces a new hybrid approach to enhance fetal health classification using an intelligent and dynamic combination of Random Forest (RF) and AdaBoost machine learning algorithms. The proposed work includes a detailed review of existing models and the challenges in handling fetal health data, setting the foundation for the design of advanced hybrid models. The implemented algorithm effectively integrates the strengths of RF and AdaBoost to enhance fetal health monitoring and classification performance. The RF algorithm is widely established for its capacity to manage large and highly dimensional data sets, whereas AdaBoost focuses on enhancing classification accuracy by correcting for mistakes in the RF models' predictions.

Results: The proposed hybrid model is tested on a recognized benchmark CTG dataset, where it attained a classification accuracy of 95.98%, a precision of 92.88%, a recall of 92.78% and an F1 score of 92.70%. Achieved results demonstrate the potential of our novel approach in real-world applications, offering a promising tool for early detection of fetal anomalies, which is crucial for both fetal and maternal health.

Conclusions: Fetal health classification and timely prediction of fetal diseases seem to be a critical step throughout pregnancy. So, to deal with this problem, an attempt has been made to propose an accurate, reliable, and novel hybrid approach for enhancing fetal health classification. By combining the strengths of two algorithms, named RF and AdaBoost, superior classification accuracy, precision, F1 score, and recall have been achieved, and much better robustness compared to standalone models. We have strived to make a noteworthy impact on the health sector by developing this hybrid model for the timely evaluation and prediction of fetal-maternal health.